
import pandas as pd
import matplotlib.pyplot as plt

# Importing libraries for data preprocessing and clustering
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans

# Suppress warnings 
import warnings
warnings.filterwarnings('ignore')


# Read the dataset from a CSV file into a pandas DataFrame
df_pre = pd.read_csv("dataset.csv")


df_pre


# Checking the shape of the DataFrame 
df_pre.shape

(114000, 21)


# List of genres to be dropped from the DataFrame
genres_drop = ['ska', 'trip-hop', 'grindcore', 'death-metal', 'metalcore', 'honky-tonk','detroit-techno', 'black-metal', 'new-age', 'sertanejo', 
               'world-music', 'singer-songwriter', 'j-idol', 'j-dance', 'j-rock', 'j-pop','indian', 'bluegrass', 'breakbeat', 'chicago-house', 
               'malay', 'cantopop', 'mandopop', 'rockabilly',  'kids',  'children', 'german', 'progressive-house', 'hardstyle', 'minimal-techno', 
               'mpb', 'study', 'pop-film', 'pagode', 'turkish', 'tango', 'swedish', 'show-tunes', 'anime', 'power-pop', 'dub', 'idm', 'rock-n-roll', 
               'samba', 'sleep']


df = df_pre[~df_pre['track_genre'].isin(genres_drop)]

df


df.shape

(69000, 21)


df.dropna(inplace=True)

df


df.shape

(68999, 21)


df.describe()


import seaborn as sns


sns.set_style("darkgrid") 
  
# Identifying numerical columns 
numerical_columns = df.select_dtypes(include=["int64", "float64"]).columns 
  
# Plotting distribution of each numerical feature 
plt.figure(figsize=(14, len(numerical_columns) * 3)) 
for idx, feature in enumerate(numerical_columns, 1): 
    plt.subplot(len(numerical_columns), 2, idx) 
    sns.histplot(df[feature], kde=True) 
    plt.title(f"{feature} | Skewness: {round(df[feature].skew(), 2)}") 
  
# To adjust layout and show plots 
plt.tight_layout() 
plt.show()


# Color palette for seaborn plots
sns.set_palette("Pastel1")

# Figure for the pair plot
plt.figure(figsize=(10, 6))

# Pair plot 
sns.pairplot(df)

#TITLE
plt.suptitle('Pair Plot for DataFrame')

# Save as an image
plt.savefig('pair_plot.png')

# show
plt.show()

<Figure size 1000x600 with 0 Axes>


sns.set(style="whitegrid")

# Only numeric columns from the DataFrame
numeric_df = df.select_dtypes(include=['float64', 'int64'])

# Correlation matrix
correlation_matrix = numeric_df.corr()

colors = sns.color_palette('viridis')

# Plotting the Correlation Matrix
plt.figure(figsize=(12, 10))

sns.heatmap(correlation_matrix, annot=True, cmap='viridis', fmt=".2f", linewidths=0.5, vmin=-1, vmax=1, cbar_kws={'label': 'Correlation'}, annot_kws={"size": 10}, square=True)

plt.title('Correlation Matrix', fontsize=16, fontweight='bold')
plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability if needed
plt.yticks(rotation=0)
plt.show()


df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 68999 entries, 0 to 110999
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   Unnamed: 0        68999 non-null  int64  
 1   track_id          68999 non-null  object 
 2   artists           68999 non-null  object 
 3   album_name        68999 non-null  object 
 4   track_name        68999 non-null  object 
 5   popularity        68999 non-null  int64  
 6   duration_ms       68999 non-null  int64  
 7   explicit          68999 non-null  bool   
 8   danceability      68999 non-null  float64
 9   energy            68999 non-null  float64
 10  key               68999 non-null  int64  
 11  loudness          68999 non-null  float64
 12  mode              68999 non-null  int64  
 13  speechiness       68999 non-null  float64
 14  acousticness      68999 non-null  float64
 15  instrumentalness  68999 non-null  float64
 16  liveness          68999 non-null  float64
 17  valence           68999 non-null  float64
 18  tempo             68999 non-null  float64
 19  time_signature    68999 non-null  int64  
 20  track_genre       68999 non-null  object 
dtypes: bool(1), float64(9), int64(6), object(5)
memory usage: 11.1+ MB


df = df.drop(['Unnamed: 0','popularity', 'instrumentalness', 'explicit', 'duration_ms', 'key'], axis=1)


df.shape

(68999, 15)


scaler = StandardScaler()


number_cols = ['valence', 'acousticness', 'danceability', 'energy', 'liveness', 'loudness', 'mode', 'speechiness', 'tempo']


df[number_cols] = scaler.fit_transform(df[number_cols])

df


def optimise_k_means(data, max_k):
    means = []
    inertias = []
    
    for k in range(1, max_k):
        kmeans = KMeans(n_clusters=k)
        kmeans.fit(data)
        
        means.append(k)
        inertias.append(kmeans.inertia_)
        
    # Generate the elbow plot
    fig = plt.subplots(figsize=(10,5))
    plt.plot(means, inertias, 'o-')
    plt.xlabel('Number of Clusters')
    plt.ylabel('Inertia')
    plt.grid(True)
    plt.show()


optimise_k_means(df[number_cols], 10)


kmeans = KMeans(n_clusters=7)


kmeans.fit(df[number_cols])

KMeans(n_clusters=7)

KMeans(n_clusters=7)


df['kmeans_7'] = kmeans.labels_

df


# Visualizing the Clusters with t-SNE
import plotly.express as px

from sklearn.manifold import TSNE
from sklearn.pipeline import Pipeline

# Pipeline to standardize the data and then apply t-SNE
tsne_pipeline = Pipeline([('scaler', StandardScaler()), ('tsne', TSNE(n_components=2, verbose=1))])

# Fiting the pipeline to the data and transform it to get the t-SNE embedding
genre_embedding = tsne_pipeline.fit_transform(df[number_cols])

# DataFrame to hold the t-SNE projection
projection = pd.DataFrame(columns=['x', 'y'], data=genre_embedding)

# Adding the genre and cluster information
projection['genres'] = df['track_genre']
projection['cluster'] = df['kmeans_7']

# Scatter plot to visualize the t-SNE projection colored by clusters
fig = px.scatter(
    projection, x='x', y='y', color='cluster', hover_data=['x', 'y', 'genres'])
fig.show()

[t-SNE] Computing 91 nearest neighbors...
[t-SNE] Indexed 68999 samples in 0.102s...
[t-SNE] Computed neighbors for 68999 samples in 22.347s...
[t-SNE] Computed conditional probabilities for sample 1000 / 68999
[t-SNE] Computed conditional probabilities for sample 2000 / 68999
[t-SNE] Computed conditional probabilities for sample 3000 / 68999
[t-SNE] Computed conditional probabilities for sample 4000 / 68999
[t-SNE] Computed conditional probabilities for sample 5000 / 68999
[t-SNE] Computed conditional probabilities for sample 6000 / 68999
[t-SNE] Computed conditional probabilities for sample 7000 / 68999
[t-SNE] Computed conditional probabilities for sample 8000 / 68999
[t-SNE] Computed conditional probabilities for sample 9000 / 68999
[t-SNE] Computed conditional probabilities for sample 10000 / 68999
[t-SNE] Computed conditional probabilities for sample 11000 / 68999
[t-SNE] Computed conditional probabilities for sample 12000 / 68999
[t-SNE] Computed conditional probabilities for sample 13000 / 68999
[t-SNE] Computed conditional probabilities for sample 14000 / 68999
[t-SNE] Computed conditional probabilities for sample 15000 / 68999
[t-SNE] Computed conditional probabilities for sample 16000 / 68999
[t-SNE] Computed conditional probabilities for sample 17000 / 68999
[t-SNE] Computed conditional probabilities for sample 18000 / 68999
[t-SNE] Computed conditional probabilities for sample 19000 / 68999
[t-SNE] Computed conditional probabilities for sample 20000 / 68999
[t-SNE] Computed conditional probabilities for sample 21000 / 68999
[t-SNE] Computed conditional probabilities for sample 22000 / 68999
[t-SNE] Computed conditional probabilities for sample 23000 / 68999
[t-SNE] Computed conditional probabilities for sample 24000 / 68999
[t-SNE] Computed conditional probabilities for sample 25000 / 68999
[t-SNE] Computed conditional probabilities for sample 26000 / 68999
[t-SNE] Computed conditional probabilities for sample 27000 / 68999
[t-SNE] Computed conditional probabilities for sample 28000 / 68999
[t-SNE] Computed conditional probabilities for sample 29000 / 68999
[t-SNE] Computed conditional probabilities for sample 30000 / 68999
[t-SNE] Computed conditional probabilities for sample 31000 / 68999
[t-SNE] Computed conditional probabilities for sample 32000 / 68999
[t-SNE] Computed conditional probabilities for sample 33000 / 68999
[t-SNE] Computed conditional probabilities for sample 34000 / 68999
[t-SNE] Computed conditional probabilities for sample 35000 / 68999
[t-SNE] Computed conditional probabilities for sample 36000 / 68999
[t-SNE] Computed conditional probabilities for sample 37000 / 68999
[t-SNE] Computed conditional probabilities for sample 38000 / 68999
[t-SNE] Computed conditional probabilities for sample 39000 / 68999
[t-SNE] Computed conditional probabilities for sample 40000 / 68999
[t-SNE] Computed conditional probabilities for sample 41000 / 68999
[t-SNE] Computed conditional probabilities for sample 42000 / 68999
[t-SNE] Computed conditional probabilities for sample 43000 / 68999
[t-SNE] Computed conditional probabilities for sample 44000 / 68999
[t-SNE] Computed conditional probabilities for sample 45000 / 68999
[t-SNE] Computed conditional probabilities for sample 46000 / 68999
[t-SNE] Computed conditional probabilities for sample 47000 / 68999
[t-SNE] Computed conditional probabilities for sample 48000 / 68999
[t-SNE] Computed conditional probabilities for sample 49000 / 68999
[t-SNE] Computed conditional probabilities for sample 50000 / 68999
[t-SNE] Computed conditional probabilities for sample 51000 / 68999
[t-SNE] Computed conditional probabilities for sample 52000 / 68999
[t-SNE] Computed conditional probabilities for sample 53000 / 68999
[t-SNE] Computed conditional probabilities for sample 54000 / 68999
[t-SNE] Computed conditional probabilities for sample 55000 / 68999
[t-SNE] Computed conditional probabilities for sample 56000 / 68999
[t-SNE] Computed conditional probabilities for sample 57000 / 68999
[t-SNE] Computed conditional probabilities for sample 58000 / 68999
[t-SNE] Computed conditional probabilities for sample 59000 / 68999
[t-SNE] Computed conditional probabilities for sample 60000 / 68999
[t-SNE] Computed conditional probabilities for sample 61000 / 68999
[t-SNE] Computed conditional probabilities for sample 62000 / 68999
[t-SNE] Computed conditional probabilities for sample 63000 / 68999
[t-SNE] Computed conditional probabilities for sample 64000 / 68999
[t-SNE] Computed conditional probabilities for sample 65000 / 68999
[t-SNE] Computed conditional probabilities for sample 66000 / 68999
[t-SNE] Computed conditional probabilities for sample 67000 / 68999
[t-SNE] Computed conditional probabilities for sample 68000 / 68999
[t-SNE] Computed conditional probabilities for sample 68999 / 68999
[t-SNE] Mean sigma: 0.000000
[t-SNE] KL divergence after 250 iterations with early exaggeration: 101.086151
[t-SNE] KL divergence after 1000 iterations: 2.287273


import numpy as np
# Song clustering Pipeline
song_cluster_pipeline = Pipeline([
    ('scaler', StandardScaler()),  # Step 1: Standardizing
    ('kmeans', KMeans(n_clusters=7, verbose=False))  #K-means clustering
], verbose=False)

# Select numerical columns from the data
X = df.select_dtypes(np.number)
number_cols = list(X.columns)

# Fit the pipeline to the data
song_cluster_pipeline.fit(X)

# Predict cluster labels for the data
song_cluster_labels = song_cluster_pipeline.predict(X)

# Assign cluster labels to the original dataset
df['cluster_label'] = song_cluster_labels


# Visualizing the Clusters with PCA

from sklearn.decomposition import PCA

# Pipeline for PCA
pca_pipeline = Pipeline([('scaler', StandardScaler()), ('PCA', PCA(n_components=2))])

# FitING the pipeline to the data and transform it to get the PCA embedding
song_embedding = pca_pipeline.fit_transform(X)
# DataFrame to hold the PCA projection
projection = pd.DataFrame(song_embedding, columns=['x', 'y'])

# track title and cluster information to the projection DataFrame
projection['title'] = df['track_name']
projection['cluster'] = df['cluster_label']

# Scatter plot to visualize the PCA projection colored by clusters
fig = px.scatter(
    projection, x='x', y='y', color='cluster', hover_data=['x', 'y', 'title'])
fig.show()


import spotipy
from spotipy.oauth2 import SpotifyClientCredentials

# Replace 'secret' with  Spotify client secret
cid = '778a29e66e464a668d6ed5419a05ec3d'
secret = '937d60c66cae42a980dcdbd9fe4968bd'

# Client credentials manager
client_credentials_manager = SpotifyClientCredentials(client_id=cid, client_secret=secret)

# Spotify client
sp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)


from collections import defaultdict
import pandas as pd

def find_song(artist, track_name):
    # Initializing a defaultdict
    song_data = defaultdict(list)

    # Filter the DataFrame by artist and track name
    song_info = df[(df['track_name'] == track_name) & (df['artists'] == artist)]

    # If no matching song is found, return None
    if song_info.empty:
        return None

    # Adding song information to the dictionary
    song_data['name'].append(song_info.iloc[0]['track_name'])
    song_data['artists'].append(song_info.iloc[0]['artists'])

    # Adding audio features to the dictionary
    for key in ['acousticness', 'danceability', 'energy', 'liveness', 'loudness', 'mode', 'speechiness', 'tempo', 'valence']:
        song_data[key].append(song_info.iloc[0][key])

    # Creating a DataFrame from the dictionary
    return pd.DataFrame(song_data)

# Example usage:
artist_name = "Nirvana"
track_name = "Come As You Are"
song_details = find_song(artist_name, track_name)
print(song_details)

              name  artists  acousticness  danceability    energy  liveness  \
0  Come As You Are  Nirvana     -0.918685     -0.450929  0.718854 -0.625801   

   loudness      mode  speechiness    tempo   valence  
0  0.433696 -1.296546    -0.415584 -0.06467  0.240484


from collections import defaultdict
from sklearn.metrics import pairwise_distances
from sklearn.impute import SimpleImputer
import numpy as np


# Function to get song data from the dataset
def get_song_data(song, your_data):
    try:
        # Find the song in the dataset
        song_data = your_data[(your_data['track_name'] == song['name']) & (your_data['artists'] == song['artist'])].iloc[0]
        return song_data
    except IndexError:
        return None

    
# Function to calculate the mean vector of a list of songs
def get_mean_vector(song_list, your_data):
    song_vectors = []
    imputer = SimpleImputer(strategy='mean') # Impute missing values with the mean
    
    for song in song_list:
        song_data = get_song_data(song, your_data)
        if song_data is None:
            print('Warning: {} by {} does not exist in your dataset'.format(song['name'], song['artist']))
            continue
        # Impute missing values and flatten the song vector
        song_data_imputed = imputer.fit_transform(song_data[number_cols].values.reshape(1, -1))
        song_vectors.append(song_data_imputed.flatten())
    # Create a matrix from song vectors and calculate the mean vector    
    song_matrix = np.array(list(song_vectors))
    return np.mean(song_matrix, axis=0)

# Function to flatten a list of dictionaries into a dictionary of lists
def flatten_dict_list(dict_list):
    flattened_dict = defaultdict(list)
    for key in dict_list[0].keys():
        flattened_dict[key] = []
    
    for dictionary in dict_list:
        for key, value in dictionary.items():
            flattened_dict[key].append(value)
            
    return flattened_dict

# Function to recommend songs based on a list of input songs
def recommend_songs(song_list, your_data, n_songs=10):
    metadata_cols = ['track_name', 'artists']
    song_dict = flatten_dict_list(song_list)
    
    # Calculate the mean vector for the input songs
    song_center = get_mean_vector(song_list, your_data)
    scaler = song_cluster_pipeline.steps[0][1]
    # Only include relevant columns for scaling
    scaled_data = scaler.transform(your_data[number_cols])
    scaled_song_center = scaler.transform(song_center.reshape(1, -1))
    distances = pairwise_distances(scaled_song_center, scaled_data, metric='cosine')
    index = list(np.argsort(distances)[:, :n_songs][0])
    
    rec_songs = your_data.iloc[index]
    rec_songs = rec_songs[~rec_songs['track_name'].isin(song_dict['name']) & ~rec_songs['artists'].isin(song_dict['artist'])]
    return rec_songs[metadata_cols].to_dict(orient='records')

# Example usage:
song_list = [{'name': 'Moonlight', 'artist': 'XXXTENTACION'}, 
             {'name': 'PTSD', 'artist': 'G Herbo'},
             {'name': 'Lucid Dreams', 'artist': 'Juice WRLD'}]  # List of songs

# Get recommended songs
recommended_songs = recommend_songs(song_list, df, n_songs=10)
print("Recommended Songs:")
for index, song in enumerate(recommended_songs, start=1):
    print(f"{index}. {song['track_name']} by {song['artists']}")

Warning: PTSD by G Herbo does not exist in your dataset
Warning: Lucid Dreams by Juice WRLD does not exist in your dataset
Recommended Songs:
1. 03' by Sainte
2. Ganha o Mundo by MC Hariel;Mc Dimenor Dr
3. Teu Herói by Weliton O Gordinho;Thiago Aquino
4. Passive Aggressive by Nate Traveller
5. bingo by Don L
6. Rebola Lentin (feat. Mc Kaio) by Bonde do gato preto;Mc Kaio
7. Sento de Repente by Bonde do gato preto

	Unnamed: 0	track_id	artists	album_name	track_name	popularity	duration_ms	explicit	danceability	energy	...	loudness	mode	speechiness	acousticness	instrumentalness	liveness	valence	tempo	time_signature	track_genre
0	0	5SuOikwiRyPMVoIQDJUgSV	Gen Hoshino	Comedy	Comedy	73	230666	False	0.676	0.4610	...	-6.746	0	0.1430	0.0322	0.000001	0.3580	0.7150	87.917	4	acoustic
1	1	4qPNDBW1i3p13qLCt0Ki3A	Ben Woodward	Ghost (Acoustic)	Ghost - Acoustic	55	149610	False	0.420	0.1660	...	-17.235	1	0.0763	0.9240	0.000006	0.1010	0.2670	77.489	4	acoustic
2	2	1iJBSr7s7jYXzM8EGcbK5b	Ingrid Michaelson;ZAYN	To Begin Again	To Begin Again	57	210826	False	0.438	0.3590	...	-9.734	1	0.0557	0.2100	0.000000	0.1170	0.1200	76.332	4	acoustic
3	3	6lfxq3CG4xtTiEg7opyCyx	Kina Grannis	Crazy Rich Asians (Original Motion Picture Sou...	Can't Help Falling In Love	71	201933	False	0.266	0.0596	...	-18.515	1	0.0363	0.9050	0.000071	0.1320	0.1430	181.740	3	acoustic
4	4	5vjLSffimiIP26QG5WcN2K	Chord Overstreet	Hold On	Hold On	82	198853	False	0.618	0.4430	...	-9.681	1	0.0526	0.4690	0.000000	0.0829	0.1670	119.949	4	acoustic
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
113995	113995	2C3TZjDRiAzdyViavDJ217	Rainy Lullaby	#mindfulness - Soft Rain for Mindful Meditatio...	Sleep My Little Boy	21	384999	False	0.172	0.2350	...	-16.393	1	0.0422	0.6400	0.928000	0.0863	0.0339	125.995	5	world-music
113996	113996	1hIz5L4IB9hN3WRYPOCGPw	Rainy Lullaby	#mindfulness - Soft Rain for Mindful Meditatio...	Water Into Light	22	385000	False	0.174	0.1170	...	-18.318	0	0.0401	0.9940	0.976000	0.1050	0.0350	85.239	4	world-music
113997	113997	6x8ZfSoqDjuNa5SVP5QjvX	Cesária Evora	Best Of	Miss Perfumado	22	271466	False	0.629	0.3290	...	-10.895	0	0.0420	0.8670	0.000000	0.0839	0.7430	132.378	4	world-music
113998	113998	2e6sXL2bYv4bSz6VTdnfLs	Michael W. Smith	Change Your World	Friends	41	283893	False	0.587	0.5060	...	-10.889	1	0.0297	0.3810	0.000000	0.2700	0.4130	135.960	4	world-music
113999	113999	2hETkH7cOfqmz3LqZDHZf5	Cesária Evora	Miss Perfumado	Barbincor	22	241826	False	0.526	0.4870	...	-10.204	0	0.0725	0.6810	0.000000	0.0893	0.7080	79.198	4	world-music

	Unnamed: 0	track_id	artists	album_name	track_name	popularity	duration_ms	explicit	danceability	energy	...	loudness	mode	speechiness	acousticness	instrumentalness	liveness	valence	tempo	time_signature	track_genre
0	0	5SuOikwiRyPMVoIQDJUgSV	Gen Hoshino	Comedy	Comedy	73	230666	False	0.676	0.4610	...	-6.746	0	0.1430	0.032200	0.000001	0.3580	0.7150	87.917	4	acoustic
1	1	4qPNDBW1i3p13qLCt0Ki3A	Ben Woodward	Ghost (Acoustic)	Ghost - Acoustic	55	149610	False	0.420	0.1660	...	-17.235	1	0.0763	0.924000	0.000006	0.1010	0.2670	77.489	4	acoustic
2	2	1iJBSr7s7jYXzM8EGcbK5b	Ingrid Michaelson;ZAYN	To Begin Again	To Begin Again	57	210826	False	0.438	0.3590	...	-9.734	1	0.0557	0.210000	0.000000	0.1170	0.1200	76.332	4	acoustic
3	3	6lfxq3CG4xtTiEg7opyCyx	Kina Grannis	Crazy Rich Asians (Original Motion Picture Sou...	Can't Help Falling In Love	71	201933	False	0.266	0.0596	...	-18.515	1	0.0363	0.905000	0.000071	0.1320	0.1430	181.740	3	acoustic
4	4	5vjLSffimiIP26QG5WcN2K	Chord Overstreet	Hold On	Hold On	82	198853	False	0.618	0.4430	...	-9.681	1	0.0526	0.469000	0.000000	0.0829	0.1670	119.949	4	acoustic
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
110995	110995	7sLknEg8aVr0m5ZuCja7b3	NG Rezonance;PHD	Syncopy Radio Edits, Vol. 1	Divergence - Radio Edit	28	185142	False	0.148	0.9930	...	-7.696	0	0.0922	0.009700	0.937000	0.0376	0.0928	140.001	4	trance
110996	110996	6veycwSGozeHSFQ6fbr5dC	NG Rezonance;PHD	Syncopy Radio Edits, Vol. 1	Forgotten - Radio Edit	28	193714	False	0.504	0.9850	...	-7.305	1	0.0504	0.000810	0.922000	0.1250	0.3830	139.978	4	trance
110997	110997	0MLEzWJQcRkc5IMAqucPbV	NG Rezonance;Begbie	Syncopy Radio Edits, Vol. 1	Feel The Panic - Radio Edit	28	209600	False	0.474	0.9950	...	-4.265	1	0.0979	0.000166	0.369000	0.1500	0.0634	150.002	4	trance
110998	110998	0cRNPYxzXLNLQd1g4kKYS6	NG Rezonance	Syncopy Radio Edits, Vol. 1	Fate - Instrumental Radio Edit	28	134800	False	0.416	0.9810	...	-3.653	0	0.0943	0.000079	0.928000	0.1870	0.0662	150.054	3	trance
110999	110999	2dDE3WCSj2cELFYO1IfECD	NG Rezonance	Syncopy Radio Edits, Vol. 1	Deception - NG Rezonance 2013 Radio Edit	28	162206	False	0.469	0.9870	...	-5.525	0	0.0796	0.000055	0.932000	0.3080	0.2430	145.000	4	trance

	Unnamed: 0	track_id	artists	album_name	track_name	popularity	duration_ms	explicit	danceability	energy	...	loudness	mode	speechiness	acousticness	instrumentalness	liveness	valence	tempo	time_signature	track_genre
0	0	5SuOikwiRyPMVoIQDJUgSV	Gen Hoshino	Comedy	Comedy	73	230666	False	0.676	0.4610	...	-6.746	0	0.1430	0.032200	0.000001	0.3580	0.7150	87.917	4	acoustic
1	1	4qPNDBW1i3p13qLCt0Ki3A	Ben Woodward	Ghost (Acoustic)	Ghost - Acoustic	55	149610	False	0.420	0.1660	...	-17.235	1	0.0763	0.924000	0.000006	0.1010	0.2670	77.489	4	acoustic
2	2	1iJBSr7s7jYXzM8EGcbK5b	Ingrid Michaelson;ZAYN	To Begin Again	To Begin Again	57	210826	False	0.438	0.3590	...	-9.734	1	0.0557	0.210000	0.000000	0.1170	0.1200	76.332	4	acoustic
3	3	6lfxq3CG4xtTiEg7opyCyx	Kina Grannis	Crazy Rich Asians (Original Motion Picture Sou...	Can't Help Falling In Love	71	201933	False	0.266	0.0596	...	-18.515	1	0.0363	0.905000	0.000071	0.1320	0.1430	181.740	3	acoustic
4	4	5vjLSffimiIP26QG5WcN2K	Chord Overstreet	Hold On	Hold On	82	198853	False	0.618	0.4430	...	-9.681	1	0.0526	0.469000	0.000000	0.0829	0.1670	119.949	4	acoustic
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
110995	110995	7sLknEg8aVr0m5ZuCja7b3	NG Rezonance;PHD	Syncopy Radio Edits, Vol. 1	Divergence - Radio Edit	28	185142	False	0.148	0.9930	...	-7.696	0	0.0922	0.009700	0.937000	0.0376	0.0928	140.001	4	trance
110996	110996	6veycwSGozeHSFQ6fbr5dC	NG Rezonance;PHD	Syncopy Radio Edits, Vol. 1	Forgotten - Radio Edit	28	193714	False	0.504	0.9850	...	-7.305	1	0.0504	0.000810	0.922000	0.1250	0.3830	139.978	4	trance
110997	110997	0MLEzWJQcRkc5IMAqucPbV	NG Rezonance;Begbie	Syncopy Radio Edits, Vol. 1	Feel The Panic - Radio Edit	28	209600	False	0.474	0.9950	...	-4.265	1	0.0979	0.000166	0.369000	0.1500	0.0634	150.002	4	trance
110998	110998	0cRNPYxzXLNLQd1g4kKYS6	NG Rezonance	Syncopy Radio Edits, Vol. 1	Fate - Instrumental Radio Edit	28	134800	False	0.416	0.9810	...	-3.653	0	0.0943	0.000079	0.928000	0.1870	0.0662	150.054	3	trance
110999	110999	2dDE3WCSj2cELFYO1IfECD	NG Rezonance	Syncopy Radio Edits, Vol. 1	Deception - NG Rezonance 2013 Radio Edit	28	162206	False	0.469	0.9870	...	-5.525	0	0.0796	0.000055	0.932000	0.3080	0.2430	145.000	4	trance

	Unnamed: 0	popularity	duration_ms	danceability	energy	key	loudness	mode	speechiness	acousticness	instrumentalness	liveness	valence	tempo	time_signature
count	68999.000000	68999.000000	6.899900e+04	68999.000000	68999.000000	68999.000000	68999.000000	68999.000000	68999.000000	68999.000000	68999.000000	68999.000000	68999.000000	68999.000000	68999.000000
mean	51426.826476	33.580907	2.252585e+05	0.576138	0.645810	5.325584	-7.996436	0.627009	0.088281	0.302674	0.129505	0.204778	0.477796	122.057113	3.911129
std	31499.289759	24.772096	9.418724e+04	0.168848	0.247882	3.556244	4.958435	0.483603	0.119065	0.329293	0.283917	0.180854	0.254506	29.876655	0.405602
min	0.000000	0.000000	8.586000e+03	0.000000	0.000020	0.000000	-43.957000	0.000000	0.000000	0.000001	0.000000	0.009250	0.000000	0.000000	0.000000
25%	26249.500000	4.000000	1.755505e+05	0.465000	0.488000	2.000000	-9.517500	0.000000	0.036200	0.018500	0.000000	0.096900	0.268000	98.299500	4.000000
50%	46499.000000	35.000000	2.113410e+05	0.588000	0.694000	5.000000	-6.669000	1.000000	0.049100	0.152000	0.000029	0.129000	0.468000	121.820000	4.000000
75%	79749.500000	54.000000	2.560000e+05	0.703000	0.849000	8.000000	-4.839000	1.000000	0.084500	0.558000	0.018200	0.258000	0.682500	140.044000	4.000000
max	110999.000000	100.000000	4.246206e+06	0.980000	1.000000	11.000000	4.532000	1.000000	0.965000	0.996000	0.999000	0.997000	0.995000	243.372000	5.000000

	track_id	artists	album_name	track_name	danceability	energy	loudness	mode	speechiness	acousticness	liveness	valence	tempo	time_signature	track_genre
0	5SuOikwiRyPMVoIQDJUgSV	Gen Hoshino	Comedy	Comedy	0.591433	-0.745560	0.252185	-1.296546	0.459572	-0.821385	0.847224	0.932025	-1.142710	4	acoustic
1	4qPNDBW1i3p13qLCt0Ki3A	Ben Woodward	Ghost (Acoustic)	Ghost - Acoustic	-0.924730	-1.935649	-1.863215	0.771280	-0.100629	1.886861	-0.573825	-0.828263	-1.491748	4	acoustic
2	1iJBSr7s7jYXzM8EGcbK5b	Ingrid Michaelson;ZAYN	To Begin Again	To Begin Again	-0.818125	-1.157048	-0.350428	0.771280	-0.273644	-0.281437	-0.485355	-1.405857	-1.530474	4	acoustic
3	6lfxq3CG4xtTiEg7opyCyx	Kina Grannis	Crazy Rich Asians (Original Motion Picture Sou...	Can't Help Falling In Love	-1.836797	-2.364888	-2.121363	0.771280	-0.436581	1.829161	-0.402414	-1.315485	1.997657	3	acoustic
4	5vjLSffimiIP26QG5WcN2K	Chord Overstreet	Hold On	Hold On	0.247927	-0.818176	-0.339740	0.771280	-0.299681	0.505103	-0.673906	-1.221184	-0.070561	4	acoustic
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
110995	7sLknEg8aVr0m5ZuCja7b3	NG Rezonance;PHD	Syncopy Radio Edits, Vol. 1	Divergence - Radio Edit	-2.535654	1.400634	0.060591	-1.296546	0.032912	-0.889714	-0.924387	-1.512732	0.600603	4	trance
110996	6veycwSGozeHSFQ6fbr5dC	NG Rezonance;PHD	Syncopy Radio Edits, Vol. 1	Forgotten - Radio Edit	-0.427239	1.368360	0.139447	0.771280	-0.318158	-0.916711	-0.441120	-0.372474	0.599833	4	trance
110997	0MLEzWJQcRkc5IMAqucPbV	NG Rezonance;Begbie	Syncopy Radio Edits, Vol. 1	Feel The Panic - Radio Edit	-0.604915	1.408702	0.752549	0.771280	0.080785	-0.918667	-0.302886	-1.628251	0.935349	4	trance
110998	0cRNPYxzXLNLQd1g4kKYS6	NG Rezonance	Syncopy Radio Edits, Vol. 1	Fate - Instrumental Radio Edit	-0.948420	1.352224	0.875975	-1.296546	0.050550	-0.918932	-0.098299	-1.617249	0.937089	3	trance
110999	2dDE3WCSj2cELFYO1IfECD	NG Rezonance	Syncopy Radio Edits, Vol. 1	Deception - NG Rezonance 2013 Radio Edit	-0.634527	1.376429	0.498434	-1.296546	-0.072913	-0.919005	0.570755	-0.922564	0.767926	4	trance

Reading the dataset¶

Exploratory Data Analysis¶

Dropping genres¶

Dropping NaN values¶

left with this¶

KDE Plot¶

PAIR PLOT¶

Correlation Matrix Analysis¶

Dropping irrelavant columns¶

Transforming¶

Elbow Method¶

Clustering¶

Visualisation¶

Recommendation¶

Spotipy¶